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Deep Learning Applications in Manned Spaceflight

ABSTRACT:

Human spaceflight is deeply rooted in close collaboration between Mission Control and flight crew. Ground control acts like a black box converting vehicle telemetry, crew natural language input, and 2D video into time management and problem-solving decisions, significantly contributing to crew and vehicle efficacy by delegating cognitive load.

As next-generation missions take humans deeper into the solar system, signal propagation delays threaten to hamper this effectively real-time cognitive tether. Deep learning technologies running locally aboard spacecraft can produce intelligent systems capable of making inference-based decisions like Mission Control without the time delay, while also augmenting crew-computer interfaces in ways not seen before.

This talk will discuss three applications leveraging deep learning on a first step toward this goal. The first is an Intelligent Personal Coach app for exercise feedback. The second is a neural network to identify external vehicle handrails in 2D images to assist crew members in identifying touch points suffering Micro Meteroid and Orbital Debris (MMOD) damage which threaten spacesuit structural integrity.

Finally, a method to generate rendered image data sets for training a neural network to identify 6 degree-of-freedom pose (used by an augmented reality procedure assistant for alignment without a QR code) will be presented.